Google Announces Kubeflow to Bring Kubernetes to Machine Learning

The fully open source project is designed to help engineers build a machine learning stack using Kubernetes.

After Kubernetes and TensorFlow, Google has now released Kubeflow, a new open source project that makes it easy to consume machine learning (ML) stacks with Kubernetes.

Kubernetes is being touted as the cloud Linux, and an increasing number of people are employing it in different use cases. Machine learning is one of the fastest growing use cases for Kubernetes, but it's quite a challenge to get the entire machine learning stack up and running.

“Building any production-ready machine learning system involves various components, often mixing vendors and hand-rolled solutions. Connecting and managing these services for even moderately sophisticated setups introduces huge barriers of complexity in adopting machine learning,” said David Aronchick and Jeremy Lewi, Project Manager and Engineer, respectively, on the Kubeflow project. “Infrastructure engineers will often spend a significant amount of time manually tweaking deployments and hand rolling solutions before a single model can be tested.”

Kubeflow solves this problem because it makes using ML stacks on Kubernetes fast and extensible. It’s hosted on GitHub, and the repository contains three components: JupyterHub, to create and manage interactive Jupyter notebooks; a TensorFlow (TF) Custom Resource Definition (CRD) that can be configured to use CPUs or GPUs and adjusted to the size of a cluster with a single setting; and a TF Serving container.

Kubeflow is a muticloud solution, and if you can run Kubernetes in your environment, you can run Kubeflow.